12 papers · ranked by Valyu relevance
Anupam Gupta, Vijaykrishna Gurunathan, Ravishankar Krishnaswamy, Amit Kumar + 1 more
'Amit Kumar' 'Sahil Singla'] e vector-balancing problem is a fundamental problem in discrepancy theory: given vectors in [−1, 1] , nd a signing () ∈ {±1} of each vector to minimize the discrepancy k Í () · k∞. is problem has been extensively studied in the static/oine seing. In this paper we initiate its study in the…
San Dinh, Claudemi A. Nascimento, David S. Mebane, Fernando V. Lima
Implementation of Dynamic Discrepancy Reduced-Order Modeling in Advanced Process Control Authors: San Dinh, Claudemi A. Nascimento, David S. Mebane, Fernando V. Lima This paper introduces a novel framework for implementing dynamic discrepancy reduced-order modeling in advanced process control. This framework balances…
Megan R. Ebers, Katherine M. Steele, J. Nathan Kutz
Physics-based and first-principles models pervade the engineering and physical sciences, allowing for the ability to model the dynamics of complex systems with a prescribed accuracy. The approximations used in deriving governing equations often result in discrepancies between the model and sensor-based measurements of…
Hippolyte Verdier, François Laurent, Alhassan Cassé, Christian L. Vestergaard + 2 more
Numerous models have been developed to account for the complex properties of the random walks of biomolecules. However, when analysing experimental data, conditions are rarely met to ensure model identification. The dynamics may simultaneously be influenced by spatial and temporal heterogeneities of the environment…
Megan R. Ebers, Michael C. Rosenberg, J. Nathan Kutz, Katherine M. Steele
We currently lack a theoretical framework capable of characterizing heterogeneous responses to exoskeleton interventions. Predicting an individual’s response to an exoskeleton and understanding what data are needed to characterize responses has been a persistent challenge. In this study, we leverage a neural…
Dai, Yuntao
This paper introduces a geometric theory of model error, treating true and model dynamics as geodesic flows generated by distinct affine connections on a smooth manifold. When these connections differ, the resulting trajectory discrepancy—termed the Latent Error Dynamic Response (LEDR)—acquires an intrinsic dynamical…
Joseph G. Shuttleworth, Chon Lok Lei, Dominic G. Whittaker, Monique J. Windley + 3 more
'Monique J. Windley' 'Adam P. Hill' 'Simon P. Preston' 'Gary R. Mirams'] When using mathematical models to make quantitative predictions for clinical or industrial use, it is important that predictions come with a reliable estimate of their accuracy (uncertainty quantification). Because models of complex biological…
Robin Umbra, Ulrike Fasbender
This manuscript introduces the Interaction Discrepancy Model (IDM), a theoretical framework designed to enhance our understanding of person-environment interactions. Traditional models often overlook the dynamic, iterative, and feedback-driven nature of these interactions, typically focusing on episodic and isolated…
Amer Kajmakovic, Konrad Diwold, Kay Römer, Jesus Pestana + 2 more
'Nermin Kajtazovic' 'Hossam A. Gabbar'] Safety-critical automation often requires redundancy to enable reliable system operation. In the context of integrating sensors into such systems, the one-out-of-two (1oo2) sensor architecture is one of the common used methods used to ensure the reliability and traceability of…
Michael Goldstein, Ian Vernon, Jonathan A. Cumming
Model or structural discrepancy is an essential component in the analysis of computer simulators, representing the differences between the outputs of the simulator and the real-world system that the simulator seeks to represent. This discrepancy can arise from various sources such as simplifications of the model…
Marina Gorostiola González, Remco L. van den Broek, Thomas G.M. Braun, Magdalini Chatzopoulou + 4 more
Proteochemometric (PCM) modelling is a powerful computational drug discovery tool used in bioactivity prediction of potential drug candidates relying on both chemical and protein information. In PCM features are computed to describe small molecules and proteins, which directly impact the quality of the predictive…
Fernando Lejarza, Michael Bâldea
Discovering the governing laws underpinning physical and chemical phenomena is a key step towards understanding and ultimately controlling systems in science and engineering. We introduce Discovery of Dynamical Systems via Moving Horizon Optimization (DySMHO), a scalable machine learning framework for identifying…